Robust Analysis Research Articles

Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities

Muscle geometry and its relevance for sports performance? A perspective of current findings and future opportunities

Design and validation of the classroom climate for an inclusive education questionnaire (CCIEQ)

This study aims to validate the Classroom Climate for an Inclusive Education Questionnaire (CCIEQ) to assess the quality of the conditions for inclusive teaching performance through three different procedures. Firstly, the study aims to evaluate the questionnaire’s content validity, culminating in the selection of nine theoretically dimensions validated quantitatively by means of an adequate Minimum Discrepancy of the Chi-square Value Divided by Degree of Freedom Index (CMIN/DF). Secondly, it seeks to assess the construct validity of the questionnaire with a Robust Exploratory Factor Analysis Technique and Hull Method to evaluate internal consistency for a final configuration of a single factor to obtain a single final score. Thirdly, the study aims to evaluate the questionnaire’s convergent validity, showing the existing correlations with other instruments previously validated for the same purpose (CEFI-R, UDL-checklist Test) using a common participant sample. To achieve these objectives, a sample of 153 in service teachers was used, recruited for the study sample from four different countries (Spain, Turkey, Latvia and Poland) through cluster sampling. The results show excellent psychometric properties and convergent validity of the CCIEQ, so its use as a scientific tool for samples of European in-service teachers is validated.

AI-Driven Models for Diagnosing and Predicting Outcomes in Lung Cancer: A Systematic Review and Meta-Analysis

(1) Background: Lung cancer’s high mortality due to late diagnosis highlights a need for early detection strategies. Artificial intelligence (AI) in healthcare, particularly for lung cancer, offers promise by analyzing medical data for early identification and personalized treatment. This systematic review evaluates AI’s performance in early lung cancer detection, analyzing its techniques, strengths, limitations, and comparative edge over traditional methods. (2) Methods: This systematic review and meta-analysis followed the PRISMA guidelines rigorously, outlining a comprehensive protocol and employing tailored search strategies across diverse databases. Two reviewers independently screened studies based on predefined criteria, ensuring the selection of high-quality data relevant to AI’s role in lung cancer detection. The extraction of key study details and performance metrics, followed by quality assessment, facilitated a robust analysis using R software (Version 4.3.0). The process, depicted via a PRISMA flow diagram, allowed for the meticulous evaluation and synthesis of the findings in this review. (3) Results: From 1024 records, 39 studies met the inclusion criteria, showcasing diverse AI model applications for lung cancer detection, emphasizing varying strengths among the studies. These findings underscore AI’s potential for early lung cancer diagnosis but highlight the need for standardization amidst study variations. The results demonstrate promising pooled sensitivity and specificity of 0.87, signifying AI’s accuracy in identifying true positives and negatives, despite the observed heterogeneity attributed to diverse study parameters. (4) Conclusions: AI demonstrates promise in early lung cancer detection, showing high accuracy levels in this systematic review. However, study variations underline the need for standardized protocols to fully leverage AI’s potential in revolutionizing early diagnosis, ultimately benefiting patients and healthcare professionals. As the field progresses, validated AI models from large-scale perspective studies will greatly benefit clinical practice and patient care in the future.

Constructing a robust data envelopment analysis model to evaluate the profitability efficiency of banks with financial loss

ABSTRACT This paper introduces a robust data envelopment analysis (DEA) model to address the limitations of traditional models in handling nonpositive data and applies it to measure the profitability efficiency of banks, including those facing financial losses. The proposed model offers several advantages over current approaches. The model is designed to resist the influence of extreme values and effectively handle variables with positive values for some banks and nonpositive values for others, can be applied under various returns-to-scale conditions and does not require any prior knowledge, overcomes the lack of translation invariance, considers nonradial input or output slack, and uses benchmark targets with economic interpretation. Numerical examples are provided to illustrate its effectiveness, and the results are compared with those obtained from commonly used methods, highlighting its superior performance in handling nonpositive data. Finally, the proposed method is applied to measure the profitability efficiency of commercial banks, demonstrating its practicality in real-world scenarios.

Labour market insecurity and volunteering in the European Union: policy suggestions for job security

PurposeThere is extensive literature on the determinants of job tenure insecurity. However, very little is known about the individual drivers of labour market insecurity. Additionally, while a piece of literature shows that volunteering improves workers' income, no study considers volunteering as an activity which could help workers to feel more confident about their perception of labour market insecurity if they lost or resigned their jobs. Therefore, purpose of this paper is to study whether workers who volunteer are less likely to perceive labour market insecurity.Design/methodology/approachThe paper employs data from the sixth European working conditions survey which provides a great deal of information on working conditions. For the empirical investigation, probit model as well as robustness analysis have been implemented.FindingsResults show that employees who do voluntary activities have a greater likelihood of declaring perceived labour market insecurity, which is nearly 3 percentage points lower, than employees who do not volunteer. Findings suggest that governments need to improve the relationship between for-profit and non-profit sectors to encourage volunteering.Originality/valueThis is the first study which considers volunteering as an activity which could help workers to feel more confident about their perception of “labour market insecurity”. Most of the studies on “labour market insecurity” do not focus on the workers individual characteristics but mainly on the labour markets institutional characteristics and welfare regimes differences.

Cost-effectiveness of a medication review intervention for general practitioners and their multimorbid older patients with polypharmacy

Older adults with multiple chronic conditions and polypharmacy are at an increased risk of having adverse health outcomes, affecting quality of life and generating costs. Primary care has to be effective to guarantee excellent treatment to these patients, who are among the most vulnerable. This project aimed to assess the cost-effectiveness of a tool aimed at improving general practitioners' (GPs) performance, namely a medication review intervention centered around an electronic clinical decision support system (eCDSS). We performed a pre-planned within-trial cost-effectiveness analysis of the OPTICA trial, a cluster randomized controlled trial in Swiss primary care practices aimed at optimizing medication appropriateness and reducing prescribing omissions. Trial participants were older adults aged ≥65 years with ≥3 chronic conditions and ≥5 medications. The 160 participants in the intervention group received a medication review intervention centered around an eCDSS provided by their GP and followed by shared decision-making with their GP. The 163 participants in the control group had a medication discussion in line with usual care with their GP. Patients were followed-up for 12 months. Considering the clustered structure of the data at GP practice level, we applied Generalized Structural Equation Models (GSEMs) on a multiple imputed sample to estimate intervention effects on costs and quality-adjusted life years (QALYs). The intervention strategy was dominant with cost-savings of CHF 1′857 (95 % confidence interval (CI): CHF -3′620 to −93, p-value <0.039, with CHF 1≅USD 1.11 as of November 2023) and a gain of 0.026 incremental QALYs (95 % CI: 0.013 to 0.040, p-value <0.001) per study participant. In robustness analyses, directions of effects were fully consistent, albeit some effect estimates non-significant. Subgroup analyses suggested stronger effects in men and older adults aged 65–74 years or aged ≥85 years. The medication review intervention led to cost savings and an improvement in quality of life, potentially resulting from an accumulation of multiple small positive intervention effects, such as fewer hospitalizations and nursing visits at home.

Efficient shrub modelling based on terrestrial laser scanning (TLS) point clouds

ABSTRACT This paper presents a methodology for automatically generating 3D models of individual shrubs from point clouds acquired by Terrestrial Laser Scanning (TLS) devices. The creation of accurate 3D models for shrubs holds significant value for ecological studies. The shrub modelling is more challenging compared to other tree modelling tasks, due to the presence of substantial noise and fuzzy branch structures. To address these challenges, our approach first partitions the input point cloud into two categories: branches and others. This point segmentation process effectively eliminates interference of leaves and noise, thereby enhancing the visibility of branch skeletons. Then, we construct a triangulation network using the branch points and establish a minimum spanning tree (MST) based on this network. Serving as the initial skeleton representation of the plant, the MST preserves the essential topological structures of the branches. Within the extracted MST, we implement a recursive trimming technique to eliminate redundant branches by merging adjacent points and edges, ultimately consolidating the skeleton structure. Finally, we employ an adaptive cylinder fitting algorithm that relies on robust principal component analysis (RPCA) to generate the model. The effectiveness and robustness of the proposed method are demonstrated via experiments on different datasets, with an average fitting error of 1.13 cm, indicating that our approach can achieve high accuracy in modelling individual shrubs.

The GLM-spectrum: A multilevel framework for spectrum analysis with covariate and confound modelling

Abstract The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling.

Comparison of resumption of ovulation after cessation of oral contraceptives and medroxyprogesterone acetate in women with polycystic ovary syndrome

Objective Both oral contraceptive pills (OCPs) and cyclic medroxyprogesterone acetate (MPA) are widely used to control menstrual abnormalities in women with polycystic ovary syndrome (PCOS). We aimed to evaluate the chance of ovulation resumption after cessation of OCPs and MPA in women with PCOS. Methods A retrospective study was conducted of women with PCOS who were treated with OCPs or cyclic MPA from September 2015 to March 2019. After cessation of medication, ovulation was assessed using basal body temperature and/or measurement of serum progesterone. The odds ratio for ovulation resumption was assessed with multivariable logistic regression. Additionally, doubly robust analysis was performed with inverse-probability-weighted analysis and regression adjustment based on the covariate balancing propensity score to adjust for the effect of covariates on the treatment assignment. Results Among 272 women with PCOS, 136 were prescribed OCPs and 136 were prescribed cyclic MPA. Ovulation resumed in 18.4% of women (n = 25) after cessation of MPA and in 24.3% of women (n = 33) after cessation of OCPs. The odds of ovulation resumption in MPA users were comparable with those in OCP users (adjusted odds ratio (aOR) 1.00, 95% confidence interval (CI) 0.89–1.12). After multiple imputation due to missing values, the results did not change substantially (aOR 0.99, 95% CI 0.89–1.10). Conclusions Among women with PCOS, MPA users have a similar chance of ovulation resumption as OCP users after cessation of medication. Cyclic MPA can be a good alternative to OCPs in women for whom OCPs are contraindicated or who decline to take OCPs.

Robust mode analysis of three-bluff-body combustion flow

Studies of combustion flows behind bluff-body configurations help optimize stability and efficacy of propulsion engines. However, theoretical and computational analyses of these systems are difficult in realistic configurations, especially for strongly driven flows. The advent of high-resolution, high-frequency imaging opens a new avenue to study these systems through data-analytic approaches. We use a recently introduced methodology, robust mode analysis (RMA), to study a series of combustion flows behind a three-bluff-body configuration. RMA identifies periodically evolving spatial structures in a flow common between multiple, nominally identical realizations of a flow and, hence, can be used to discard noise and other irregular flow facets. Our experiment records the flow in two spatial perspectives and through a set of time-series pressure measurements. Periods of robust modes, computed through the three recordings, are shown to be consistent for the entire set of experiments. A method to quantify the proximity of spatial structures of modes from different imaging viewpoints is introduced. It is used to compare robust modes in the two imaging views, to identify quasi-periodically evolving flow constituents, and to construct bifurcation diagrams for each constituent.

RPCA-AENet: Clutter Suppression and Simultaneous Stationary Scene and Moving Targets Imaging in the Presence of Motion Errors.

Clutter suppression and ground moving target imaging in synthetic aperture radar (SAR) system have been receiving increasing attention for both civilian and military applications. The problem of clutter suppression and ground moving target imaging in practical applications is much more challenging due to the motion error of the radar platform. In this article, we focus on the problems of clutter suppression and simultaneous stationary and moving target imaging in the presence of motion errors. Specifically, we propose a robust principal component analysis autoencoder network (RPCA-AENet) in a single-channel SAR system. In RPCA-AENet, the encoder transforms the SAR echo into imaging results of stationary scene and ground moving targets, and the decoder regenerates the SAR echo using the obtained imaging results. The encoder is designed by the unfolded robust principal component analysis (RPCA), while the decoder is formulated into two dense layers and one additional layer. Joint reconstruction loss, entropy loss, and measurement distance loss are utilized to guide the training of the RPCA-AENet. Notably, the algorithm operates in a totally self-supervised form and requires no other labeled SAR data. The methodology was tested on numerical SAR data. These tests show that the proposed architecture outperforms other state-of-the-art methods.

Comprehensive design optimization of vertical seismic absorbers incorporating sensitivity and robust analysis: A case study of the KDamper

This study presents a comprehensive design optimization approach for building vibration control systems, demonstrated through a case study on the KDamper implemented as a vertical seismic absorber (VSA). The methodology encompasses a series of steps to identify the most suitable VSA design while maximizing the utilization of available resources and considering model and reality imperfections. The outcomes of this investigation include a Pareto front featuring optimal structures and a set of design guidelines specifically tailored for the development of a VSA system. Moreover, optimization analyses conducted on simplified systems have revealed their potential effectiveness even without certain components. Furthermore, a bi-objective robust optimization has demonstrated the system’s resilience to manufacturing uncertainties prevalent in real-world scenarios. Finally, this approach has yielded more consistent system performance than conventional optimization approaches. The proposed methodology holds broad applicability to diverse systems, offering valuable support in achieving widespread utilization and facilitating the design of optimal structures, such as the vibration-absorption system examined in this study.

Impact of combined novel hormonal agents on testosterone levels of patients with prostate cancer undergoing treatment with leuprorelin.

95 Background: Novel hormonal agents (NHA) are part of the management of prostate cancer (PCa) since from the high-risk localized disease throughout the disease continuum, while sustained androgen deprivation therapy (ADT), through the utilization of gonadotropin-releasing hormone (GnRH) agonists or antagonists, stands as the cornerstone of this combined treatment regimen. Although deeper and earlier PSA response rates were demonstrated with these combinations, there is a lack of information on the testosterone levels during the treatment with this doublet. In this retrospective real-world study, we meticulously analyzed data on testosterone tests obtained from a prominent Brazilian institution to comprehensively assess the clinical efficacy of leuprorelin either in monotherapy or in combinations on the management of PCa. Methods: We conducted a retrospective analysis by gathering data from the medical records of pts diagnosed with PCa who were undergoing treatment with leuprorelin alone or in combination with a novel hormonal agent (NHA). Descriptive statistics was assessed by medians and comparison between groups was assessed by chi-square test. This data collection spanned a nine-year period from 2014 to 2023. Results: A total of 1,744 tests were collected from 240 individual pts. Median baseline testosterone level was 373.9 ng/mL among those treated with leuprorelin monotherapy and 368 ng/mL for those receiving leuprorelin + NHA. Testosterone levels &lt; 50 ng/mL were achieved in 776/786 tests (98.7%) of pts receiving leuprorelin and 945/958 tests (98.6%) in pts receiving leuprorelin + NHA (p=0.9). Testosterone levels &lt; 20 ng/mL were found in 633/786 tests (80.5%) of pts receiving leuprorelin and 842/958 tests (87.9%) in pts receiving leuprorelin + NHA (p &lt; 0.0001). Conclusions: In this robust institutional database analysis, we demonstrated that leuprorelin demonstrated similar levels of testosterone suppression (&lt; 50 ng/mL) both in monotherapy or combined with NHA. However, when analyzing deeper testosterone suppression levels, the addition of NHA was associated with statistically significant difference in the rate of patients with testosterone &lt; 20 ng/mL. These results reinforce the importance of intense hormonal treatment in pts with PCa receiving leuprorelin.

MINDS. JWST/MIRI Reveals a Dynamic Gas-rich Inner Disk inside the Cavity of SY Cha

SY Cha is a T Tauri star surrounded by a protoplanetary disk with a large cavity seen in the millimeter continuum but has the spectral energy distribution of a full disk. Here we report the first results from JWST/Mid-InfraRed Instrument (MIRI) Medium Resolution Spectrometer (MRS) observations taken as part of the MIRI mid-INfrared Disk Survey (MINDS) GTO Program. The much improved resolution and sensitivity of MIRI-MRS compared to Spitzer enables a robust analysis of the previously detected H2O, CO, HCN, and CO2 emission as well as a marginal detection of C2H2. We also report the first robust detection of mid-infrared OH and rovibrational CO emission in this source. The derived molecular column densities reveal the inner disk of SY Cha to be rich in both oxygen- and carbon-bearing molecules. This is in contrast to PDS 70, another protoplanetary disk with a large cavity observed with JWST, which displays much weaker line emission. In the SY Cha disk, the continuum, and potentially the line, flux varies substantially between the new JWST observations and archival Spitzer observations, indicative of a highly dynamic inner disk.

Comprehensive Performance and Robustness Analysis of Expander-Based Data Centers

Comprehensive Performance and Robustness Analysis of Expander-Based Data Centers

Drivers of geophagy by red brocket deer (Mazama americana) at Amazonian interior forest mineral licks.

Mineral licks are key ecological components of the Amazon rainforest, providing critical dietary functions for herbivorous and frugivorous mammals and birds, which help maintain the structure and function of the forest itself through seed and nutrient dispersal. One of the most frequent visitors of interior forest mineral licks in the Amazon is the red brocket deer (Mazama americana), a large-bodied ruminant frugivore and seed predator. While several hypotheses for the drivers of geophagy exist, including mineral supplementation, toxin adsorption, and habitat selection, robust data on geophagy for the red brocket deer for large numbers of mineral licks is nonexistent. We used soil data from 83 mineral licks in conjunction with camera trap data from 52 of those mineral licks and a mixed-effects modeling approach to test the three proposed hypotheses of geophagy for the red brocket deer. We found that consumed soils at mineral licks had elevated concentrations of almost all major and minor biologically active minerals measured, including Ca, Na, Mg, K, Cu, Zn, and Mn. Model results suggest that all three hypotheses hold true to some extent for the red brocket deer, with the greatest support for the mineral supplementation hypothesis, in particular with respect to Mg, Ca, Na, Cu, and Zn. This study provides critical information on the feeding ecology of the red brocket deer in the wild, and the first robust analysis of geophagy of an Amazonian mammal involving a large sample size of interior forest mineral licks.

Community Social Capital and Accounting Conservatism

ABSTRACT The combination of peer pressure and who you know in our society can be powerfully influential. Likewise, firms headquartered in high social capital locations can exhibit associated economic outcomes and financial qualities. Our study examines the impact of a critical firm-level qualitative characteristic, community social capital, on firm accounting conservatism. The results of our research show firms headquartered in areas of high community social capital tend to exhibit less accounting conservatism. These findings suggest high community social capital mitigates issues related to contracting efficiency, social disciplinary instruments, and information asymmetry, reducing the corporate impetus for using accounting conservatism. We apply alternative proxies for community social capital, and further robustness analyses include entropy balance matching and instrumental variable regressions. Our study extends and complements literature investigating the effects of various firm-specific traits affecting accounting conservatism and contributes to research examining the effect of community social capital on corporate policies. Data Availability: Data are available from the public sources cited in the text. JEL Classifications: A13; D91; M14; M41; Z13.

Climate Change and Its Impact on the Increase in Dengue Hemorrhagic Fever Cases in North Maluku Province (Case Study in Tidore Islands City)

North Maluku Province has experienced a significant increase in dengue fever cases over the past few years. The purpose of this study is to comprehensively investigate the intricate relationship between climate change and the surge in dengue fever cases in North Maluku Province. This research uses a qualitative case study approach to examine the impact of climate change on dengue fever cases in North Maluku Province. Data was analyzed through in-depth interviews with health workers, local communities, and related stakeholders using NVivo. This robust qualitative data analysis software facilitated the systematic coding, categorization, and thematic exploration of the rich insights gathered during the interviews. The findings show that climate change in this region has significantly impacted the ecology of disease vectors, rainfall patterns, and the spread and prevalence of the Aedes aegypti mosquito as the primary vector of dengue fever. Social, economic, and infrastructure factors also play an essential role in the increase in dengue fever cases. This research provides an in-depth understanding of the relationship between climate change and increased dengue fever cases in North Maluku Province. The implications of these findings underscore the urgency of implementing adaptation and mitigation strategies to address public health risks associated with climate change. This study shows climate change has increased dengue fever cases in North Maluku Province. Therefore, more effective preventive measures, coordination between institutions, and more active community participation are needed to deal with the health impacts caused by climate change.

Female in The Boardroom and Firm Performance: An Insight of Indonesia’s Firms

The purpose of this research is to determine the impact of female board members on firm performance. This study employed a purposive sampling technique with a research sample comprised of 264 non-financial companies listed on the Indonesia Stock Exchange between 2013 and 2019. As proxies for the independent variable of females in the boardroom, this study examines female directors' presence, a dummy variable for female CEOs, the proportion of female directors, and female directors' business ability. The return on assets (ROA), return on equity (ROE), and Tobin's Q are all proxies for gauging corporate performance. This study's analysis technique is robust regression analysis. Female board members had the greatest influence on firm performance, according to the findings. Overall, the female CEO dummy serves as a proxy for the strength of the company's performance.

SHAP-based insights for aerospace PHM: Temporal feature importance, dependencies, robustness, and interaction analysis

This research addresses a critical challenge in aerospace engineering: enhancing the interpretability of machine learning models for predictive maintenance. By integrating SHapley Additive exPlanations (SHAP), our approach decodes the relative importance of sensor-derived features, providing an analytical foundation for understanding engine degradation signals. We delve into the temporal shifts in feature relevance, unveiling the variable impact of sensor data over operational cycles. A rigorous assessment of SHAP's robustness further strengthens the reliability of our interpretive models in the face of data perturbations. Additionally, our nuanced analysis of feature interplay offers a comprehensive view of the factors influencing engine performance predictions. These methodological advancements equip engineers with precise, actionable insights for preemptive maintenance scheduling, directly contributing to the enhancement of aircraft safety and efficiency. The study's implications extend beyond theoretical analysis, offering a pragmatic blueprint for the application of SHAP in the ongoing pursuit of model transparency and maintenance optimization in the aerospace sector.